Abstract
Multi-view data coming from multiple ways or being presented in multiple forms, have more information than single-view data. So multi-view clustering benefits from exploiting the more information. Nonnegative matrix factorization (NMF) is an efficient method to learn low-rank approximation of nonnegative matrix of nonnegative data, but it may not be good at clustering. This paper presents a novel multi-view clustering algorithm (called MVCS) which properly combines the similarity and NMF. It aims to obtain latent features shared by multiple views with factorizations, which is a common factor matrix attained from the views and the common similarity matrix. Besides, according to the reconstruction precisions of data matrices, MVCS could adaptively learn the weight. Experiments on real-world data sets demonstrate that our approach may effectively facilitate multi-view clustering and induce superior clustering results.
This work is supported by the National Science Foundation of China (Nos. 61170111 and 61572407), the Project of National Science and Technology Support Program (No. 2015BAH19F02) and the Science and Technology Planning Project of Sichuan Province (No. 2014SZ0207).
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He, M., Yang, Y., Wang, H. (2016). Learning Latent Features for Multi-view Clustering Based on NMF. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_42
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